Last updated: April 11, 2026
From UMBC Website: https://professionalprograms.umbc.edu/data-science/masters-of-professional-studies-data-science/Â
This course provides a broad introduction to the practical side of machine-learning and data analysis. This course examines the end-to-end processing pipeline for extracting and identifying useful features that best represent data, a few of the most important machine algorithms, and evaluating their performance for modeling data. Topics covered include decision trees, logistic regression, linear discriminant analysis, linear and non-linear regression, basic functions, support vector machines, neural networks, Bayesian networks, bias/variance theory, ensemble methods, clustering, evaluation methodologies, and experiment design.
Prerequisite: DATA 601: Introduction to Data Science and enrollment in the Data Science program. Non-Data Science students may be permitted with instructor permission.
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